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Join us for an inspiring conversation with Polina Nyer, Senior FP&A Analyst for HP, whose career journey started on the basketball courts before pivoting to international business and ultimately, AI-driven finance transformation. Polina shares her fresh framework for understanding how finance and accounting professionals at different career stages experience and adopt AI—whether you're just starting out, deep into your analyst years, or leading a team through change. You'll hear practical advice on building confidence with AI, accelerating your learning, and creating supportive team cultures that turn uncertainty into opportunity. If you’re curious about real strategies for adapting to AI, managing risk, and blending experience with new technology, this is one episode you don't want to miss!
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Welcome to Count Me In. Today, I'm joined by Polina Nyer, Senior FP and A Analyst at HP. Polina's journey from competitive basketball to her current role of leading AI adoption at HP gives her a unique perspective on transformation in finance and business. In this episode, Polina breaks down her practical framework for how finance professionals from all stages, whether you're an AI native, a strategic analyst, or a leader, can successfully navigate the evolving world of artificial intelligence. She shares real stories, actionable advice, and insight into why human experience and business judgment are more valuable than ever in the age of AI.
Adam Larson:So whether you're just starting your career or guiding a team through change, this episode will leave you with strategies to upskill, adapt, and thrive in today's rapidly evolving business landscape. Let's dive right in. Well, Polina, I'm so excited to have you on the podcast today. We're going to be talking about AI and some frameworks that you were talking about. But I want to start with, to walk you back to beginning of your career.
Adam Larson:What first drew you into finance and how did that path lead you to where you are today?
Polina Nyer:Hi, Adam. Thanks for having me on the podcast. It's actually funny because I happened to stumble into finance. It wasn't my lifelong dream, which I've heard some of your other guests on the podcast describe it as their passion. But my story has a little bit of, I would say, two parts.
Polina Nyer:The first one is basketball. I grew up as a competitive athlete. My whole family is involved in some capacity in basketball, and I really genuinely thought that my future will be in sports. So I moved from Bulgaria, this is where I'm from, to Italy because I was recruited to play on a team there. When it came down to choosing a university degree, I was only in Italy for a year.
Polina Nyer:This was way before they had English speaking degrees, so I was going to have to do my bachelor's in Italian. I figured where there were numbers, I would probably do better than doing essays, and this will give me time to ramp up my language skills. I chose finance and international business partly for that reason. And the second part of the story is personal to me. My parents are educators and they worked incredibly hard throughout their career.
Polina Nyer:But as many, many teachers around the world, They were never well compensated for the importance of their work, for the society and the impact that it had on children. And I was watching that growing up and it really made a big impact on me. So when I was choosing a career, I wanted something that would give me financial stability and opportunities. Finance seemed like a field that could provide that. So I would say I didn't start passionate about finance, but once I began studying and later on working in the field, I thought that finance can open doors through essentially any career later on.
Polina Nyer:And I still believe that's true. So I had an opportunity to do internships in different countries. I worked for a nonprofit, then I had a startup experience, and eventually I joined AHP, which is my current employer. And I'll actually be celebrating ten years there very soon. Time flies.
Polina Nyer:And I stayed because my role has evolved significantly from traditional finance, accounting, FP and A, finance transformation, and now I work work across functions and I help organization with their AI adoption journey, and I still feel like I'm learning every week.
Adam Larson:Wow. That's that's an amazing story and I love I love talking to people when they don't have the traditional, oh, I I left college and I went to the big four and I did this and I did that. I like the different journeys that... Especially IMA members who have come for a whole different route and then finally find up here and and realize, hey, this is important. You know, and and going back to that, you know, personal thing with your parents is, you know, I I truly believe that we as a a human society underpay our teachers greatly.
Adam Larson:And I'm I'm in agreement with that, that that's that's a huge miss or disservice that we've done to all of our societies. But we don't wanna go on a tangent there, but I just wanna say I agree with you on that. And that's that's hugely important. You know, our our educators are bringing up the next generation, but yet we don't we don't give them enough money to, you know, maybe make a living, you know, these days. But but, you know, going back to our topic at hand, I I I think that I love your story and I really appreciate that.
Adam Larson:And, you know, it was great meeting you in person in at... In Tampa and I, May 26, and, you know, we've we've known each other for a number of years, but getting to meet in person is always a lot of fun. But there was really cool what you were talking about at at the conference was you, you know, you you shared this framework where you built three personas of how finance professionals kind of experience AI. And so maybe I was I was... We could start with, you know, you can kinda talk talk us through that framework a little bit because I feel like those three...
Adam Larson:I feel like we all kind of... All of our organizations are adopting AI at at, you know, in some way, shape, or form. And so we've all met these three personas and I and I really appreciate it. So let's start there and kind of talk a little bit about like maybe how that came together and why did you land on these three personas and kind of what led you there?
Polina Nyer:Sure. Well, first of all, really enjoyed my time at the IMA twenty six. I'm looking forward to going next year again. As you said, the value of meeting all of those amazing IMA volunteers in person is tremendous. But the framework actually came from conversations.
Polina Nyer:Conversations that I had with different professionals. They would come to me and they would ask me things that are completely different, almost like if people cared about a different thing around the same technology. And we often talk about AI as if we were all starting from the same place, but we really are not. Depending on where we are in our careers, AI may look very different. And for example, if a new graduate walks into the office, they have a very different relationship with AI than someone who has been in finance for twenty years.
Polina Nyer:And a finance leader has a completely different set of responsibilities. I realized we weren't talking about the same audience and we will go into each persona more in-depth, but the first and what I call the AI native, they come with... They're newly employed. They are so comfortable experimenting, but they really don't have the business judgment yet. And is not their fault.
Polina Nyer:They just need time to build it. The second persona is more experienced. I call them the strategic analyst. They know the business very well, but they're often the busiest people in the organization. They're balancing a tremendous workload and they're trying to figure out where AI fits in their workloads.
Polina Nyer:And then we have the leaders. So the leaders are not really concerned with personal productivity or how to use the tool. They're more concerned with the technology themselves around creating the environment to use it safely and ethically, and also how would the entire team can scale up and learn so that they can all use it and use it responsibly. So when I started looking at AI through those lenses, it really became more like three conversations. You can, of course, argue that there might be four or five and so on.
Polina Nyer:You can break it as much as you want to go into detail, but I think this is a good representation of the different kinds of professionals that we see in any organization.
Adam Larson:Well, and that can be really difficult when you're implementing because you're almost having to tell three different stories when you're trying to roll out this new framework, you're trying to roll out and people's different understandings depending where they are in their careers. So that that definitely makes it it it interesting from a training perspective because you can't just give the same training to every single person. Well, that's with any type of training, but like, I... This is... It's very interesting because it it is a specific technology that some people grew up with and then others people didn't.
Adam Larson:And you're having to you're having to mesh all that so that everybody can use the same technology.
Polina Nyer:Yeah. Yeah. This is exactly correct.
Adam Larson:So what does it look like when you're, when someone shows up, you know, just starting out versus someone who's been in the field for years, you know, when you're looking at that new technology, maybe we can talk a little bit about what that looks like in some more detail.
Polina Nyer:Yeah. And I will use examples because I think everybody can relate to those. Somebody who is just starting out, still need to learn the business. No matter what industry or line of business they're in, they really need the business acumen in order to be successful. And AI can be an incredible accelerator for them.
Polina Nyer:It can explain concepts. It can help summarize information, draft content so that you don't have to start from scratch. They can organize information, for example, meeting notes with really any information. And AI can also act as a tutor when you're trying to understand a new concept, a new process. So it gives the confidence to get started.
Polina Nyer:It almost gives you the feeling that your learning is accelerated, which it really is. Where in contrast, somebody who has been in finance for, let's say ten, twenty years, they're using AI for a different reason. They're bringing the business context that they already have with them and AI doesn't have that. And this is really where magic happens because imagine AI tells you that the gross margin declined by, I don't know, two percentage points, let's say. And a new analyst may accept that because it probably did decline.
Polina Nyer:AI is getting very accurate in calculations. However, an experienced analyst immediately starts thinking and they go, well, did this happen around the time that we changed the supplier? Or didn't we launch a product that quarter or leadership intentionally made an investment and we knew that we are going to get a hit on our gross margin, but it's going to pay off in the future. So these are things that you know just because you've been doing this job for a while. And these are questions that AI cannot invent on its own.
Polina Nyer:They come from having lived through all of these situations and the decisions that come from them. Another example is forecasting. AI may look at the past, right? And this is how forecasting has always been. We look at the past to predict the future, but we know that the future is not always the same as the past.
Polina Nyer:But it can suggest, for example, where the revenue would be headed. Somebody who has been partnering with sales may know things that AI doesn't know. For example, customer is holding on to place an order because they're waiting for funding from the government or something related to a competitor that just has entered the market and this competitor hasn't existed in the past. The information often is not in the data yet, so AI cannot bake it into the forecast, but it does have a huge impact on the future. So while AI may generate an output that is similar to what both people would see, let's say they experience it and inexperience, they treat those outputs completely differently.
Polina Nyer:And I think that that's encouraging because sometimes people worry that AI may take their jobs away, may make experience less valuable, but it's actually making experience much, much more valuable. AI is becoming better at producing information, but the human context, the business context and the judgment really remain what creates the value after all. So I see AI as something that complements our work rather than replaces it. This was really the conclusion of the talk that I gave at the IMA. It raises the floor for everyone, which is very exciting, but experience is what pushes the ceiling up.
Adam Larson:Yeah, that makes a lot of sense. So when we talk about somebody who has that experience, you know, a strong analyst, what have you seen that tends to trip them up when AI enters the picture? Because they're that strong analyst, they've been doing this for years, but then AI enters the picture and they have to use this new tool.
Polina Nyer:Yeah. Well, I actually do recognize myself in this medium bucket, sort of the strategic analyst or somebody who has the experience. I think it's very important for people in their mid career, maybe a very experienced individual contributor, maybe you are already a people manager, but we have to really resist the urge to automate everything and to use AI everywhere, which is hard because the opportunities really seem to be endless, but the value that is going to be created is not the same across the different tasks. So I say these people have also the advantage of the relationships. They have all those stakeholders that they know for a while, and probably those stakeholders go to them when information context is needed and not just the numbers.
Polina Nyer:And it's interesting that they're in a very specific position when it comes to AI. On one hand, it's huge advantage because they have what AI doesn't have, which is the experience, the business context, the judgment and the relationship. And they know this, you know, when something doesn't look right, you see a piece of dataset or a table, an image, a financial view, and you don't know yet what the problem is, but you know that it's something is wrong and you know that you have to go and investigate further. This is very valuable because we know that AI is just simply not perfect and it's probably never going to be perfect. And on the other hand, it comes to workloads, right?
Polina Nyer:Where these people tends to be the busiest people in the organization. So this is also, I think what trips them is you have a very limited amount of time to work on AI, expand your knowledge on AI. So how do you use it, Right? How do you use it? And this is actually, we are probably going to talk about this later, but this is actually what most people ask me.
Polina Nyer:Where do I start? How do I make sure, right? How do I make sure that I'm using this very limited amount of time that I have extra on top of all of my workload to learn or try or experiment with the tool that then it's going to pay off rather than just taking seven hour course on introduction to AI, which at the end you can only leverage maybe seventeen minutes of that course. Yeah.
Adam Larson:Well, can be, that can be super overwhelming because you're, you're, you're, like you're saying, you're already busy. You have so many things going on. And I guess that is a question a lot of times that I do try to ask people who are, who are helping people with AI like, Hey, where, where, where can you tell somebody, where do I start? Because like you said, a seven hour course can, you know, using ten minutes of it and where can you go find that? Like, can you go find that ten minutes somewhere else so that you can just learn that one thing and apply it right away?
Adam Larson:You know, what would be your, you know, your recommendation to somebody who's asking that question?
Polina Nyer:So, first of all, I completely understand this overwhelming feeling of everything is changing so fast and I feel like I'm behind every day.
Adam Larson:Yeah.
Polina Nyer:And this is a very, very human feeling and I think we all feel that way. Even people who are objectively, quote unquote, ahead of the standards financial analysts or an accounting professional still feel that way just because the technology literally changes overnight. And we see that through the different features that are released. Usually organizations already are using one AI tool and you would see almost every week, there would be something new that you can explore within that package of features available for this tool. So let's make this very practical, right?
Polina Nyer:So if you were to start tomorrow, right? What would you do? And I think that there are five things that people can do that really bring value, maybe not right away, but over time. And the first one is to simply use AI every day. It doesn't have to be throughout your whole day.
Polina Nyer:It can be one day you would use it for ten minutes. Another day you use it for forty minutes. It's more about the consistency rather than going and doing a six hour course on a Friday morning, right? Make it part of your daily routine. The more you use it, the more natural it will start.
Polina Nyer:I think this is where this overwhelming feeling starts decrease. And you also start to figure out where it would actually help you with your work. The second thing is to pick a recurrent task. Those are the easiest to apply AI on. So use AI to partner or something that you do all the time.
Polina Nyer:Like you can do, let's say on a Monday you prepare an update on the actuals or on the forecast. Maybe you write variances every month to actuals versus budget. On Friday, you may summarize the status of a project. Things like these are excellent use cases to get this recurrence going together with AI. You don't have to try to reinvent your whole job at once.
Polina Nyer:Find one repetitive task, improve it. And once you're comfortable with this process, then you move on to the next one. This has worked very, very well for me. The third one, it's individualorganizational, and this one would be to build a prompt library. Try different prompts, right?
Polina Nyer:Some of them work better than others. The ones that look better, write them down in your notes app so that the next time you do this task, because for sure you're going to come around and do it again, you already have the prompt and it's the prompt that you have tested against other prompts. This one is the one that actually works. So over time and surprisingly fast, you're going to find yourself with a toolkit of prompts that you can use on things that you do often without having to rewrite them or rethink them or retest them every single time. And I also think that this is really an effort that organizations need to make as well.
Polina Nyer:Imagine how much faster it would be if this prompt library already exists for everybody to use, and it can be for common financial related activities. But I think that in the future we would have a prompt library for really any department and any area of organization that it's going to be almost like an open source. You can add yours and others can add to that, but also organization controls it in a way that the quality remains very high. But we can all start by just creating hours and even if it's only four or five prompts and you use them all the time, that's already extremely valuable. Let's see what is it.
Polina Nyer:I was just four. Okay. So this goes back to our seven hour scores. I would say dedicating small amount of time every week to learn something new, and it doesn't have to be a formal course. It can be spending fifteen minutes or twenty minutes reading an article about a tool or about a feature or about a use case that somebody else has created.
Polina Nyer:It could be just testing. I see something appeared in my Copilot ChatGPT, a new feature. You know how they always give you the little box on top and they're like, oh, this. We always keep, just click, skip, skip, skip. So that you can go on and like do what you were trying to do when you entered the app, but maybe do explore that new function.
Polina Nyer:Just simply experiment small pieces of consistent learning really add up much faster that you will think over time rather than reserving those big chunks of time once in a while. And this is almost like think about it as going to the gym, right? Consistency over time compounds and you get the results, you know, you're not going to get the results today, but if you're doing it over an extended period of time, it also becomes much less stressful, I would say. You just go, you learn for, you can even set a timer. I've tried that.
Polina Nyer:It doesn't really work great for me, but people have tried that set a timer for twenty minutes. I'm going to learn AI for twenty minutes. And then you just stop when the timer is off. And my last piece of advice would be to not learn alone. There are so many people trying to figure this out right now.
Polina Nyer:All of our colleagues are, most likely our managers as well. So ask ask your teammates, how are they using AI? If you have a team meeting, maybe you can reserve some of the agenda on people showcasing how they're using it. Share prompts, for example, the prompt library. It would be a great practice to have it with your team.
Polina Nyer:That is not just your personal one. Some of the best ideas that I have discovered come from, not from webinar conferences, but just from a LinkedIn post or from a colleague that is implementing a similar process that I am in a different business line. So working and learning with others becomes an advantage for everybody. So I would say, yeah, if you do these five things over time, I think they accumulate and yeah, so use AI every day, pick one recurring task and don't move on to the next one until you have this one implemented with AI. Then build a prompt library ideally with your team, even better with your whole organization.
Polina Nyer:Dedicate a small amount of time to learn AI every week and do not learn alone.
Adam Larson:I think those are those are great. That's great advice because whether you're whether you're at any of those stages, the personas that you've outlined, following those steps will be extremely helpful because we're even if you're used to technology and you understand technology, you still need to spend time with something new to learn how to use it properly in its... In the context of your of your work, of your business. And so taking that time is is huge. Even just starting with the little things, I I love that because the little things grow over time as you're doing them, which I think is really great.
Adam Larson:You know, but when we look at from a, like, a leader's perspective, like a leader who is leading a team, maybe you can give some insight, especially, you know, sometimes leaders can underestimate how fast their team is adopting a new technology like AI or, you know, you even can wait too long to build out either training or governance around it. And, you know, I've had... We've had conversations on this podcast talking about, you know, what happens when you don't, you know, have some governance around your AI. You... It can lead to, you know, million dollar million dollar million plus dollar, like, mistakes that can be made.
Adam Larson:So like, maybe we can talk a little bit about that.
Polina Nyer:Yeah, sure. The leader really has a very different perspective because their responsibilities are essentially very different from using and learning how to use the tool. And as I said before, it's really not about personal productivity for them anymore.
Adam Larson:And
Polina Nyer:the question is shifting from how do I learn AI? What do I do to get better? What do I do to create value? To how do I help my entire organization and my entire team use AI effectively, responsibly, help them have the resources to learn and do it in a way that governance is in place with compliance and with segregation of duties. I know this is something that has been touched upon here on the podcast.
Polina Nyer:So I'm sure that if the audience wants to learn more about ethics and around AI, they can go and listen to those episodes. But the conversation is centered around, is our data safe in those tools? This is a big one. How do we validate those outputs? Every time we talk about AI, we always say, You cannot take the output that AI produces just as is.
Polina Nyer:We always need to validate it, but how do we validate its scale? What skills do we need to develop across the teams and in both priority? What do we develop first and what is it okay to wait and develop it later? And how do we encourage all these innovation and people to be excited and experiment, but also manage the risk that it comes to? I work for a publicly traded company.
Polina Nyer:There is a tremendous risk involved in somebody taking an internal document and pacing it in the public AI, Chargebee, for example, this can first lead to giant sanctions from the SEC, but also can be very detrimental to the company's performance. So I know companies are working on these kinds of issue, But for the leaders, let's say it has nothing to do with writing a good prompt. I think it's completely different conversation if someone copies, let's say the example we gave, financial information into a public AI model without understanding the company's policy to somebody needs to write those policies. And this is usually where the leaders come into place. And It is a difficult job.
Polina Nyer:This has never been done before. I think the leaders are at a very interesting intersection where they need to have enough understanding of the technology in order to be able to govern it and regulate it. At the same time, they don't have the luxury of waiting to understand all the nitty gritty and nuts and bolts of everything before they can place guardrails in place because it's simply going to be too late. So they do need some solid understanding, for example, how the tool works, what output it produces, where it tends to be strong and where it tends to fail. They need to know about hallucinations that may invent information very and present it very, very confidently.
Polina Nyer:Here, But the human validation still remains essential. So knowledge, these kinds even though it's not extremely deep, allows the leaders to establish the sensible guardrails and to make informed decisions around the governance. I think here, one, I'm a fan of reverse mentoring. I think if we take those early career individuals that we spoke about before that are very, very comfortable with AI and they experiment a lot, and because they experiment a lot, they see a lot of the outputs that AI produces. This would be a good, it's a good relationship for leaders and younger, earlier career employees to get together and have this reverse mentoring so that the leaders can see through the eyes of somebody who uses AI to do their work every day.
Polina Nyer:And at the same time, the leaders can provide this business judgment, the ethical perspective, which probably the junior employee wouldn't have thought of and wouldn't have been educated on yet. So that's a really, really powerful combination when both sides are learning from each other and then the learning it's taken on to creating a valuable policy for the organization. And this is also what great leadership looks like in the age of AI. It's not about having all the answers because nobody does, not only leaders, nobody really does. It's about creating the culture where people can experiment safely, share their learning, and do what's responsible, compliant, and within the organizational values.
Adam Larson:I think that was a great answer to that question because so many times organizations are kind of floundering all around and they don't give themselves the grace to say, hey, let's play with this a little bit so we can kind of figure out because you're writing a policy for something that didn't exist three, four years ago. And so there's not a precedent. It's not like the the AI companies came out with like, okay, here's our AI tool and here's a policy that can help govern it within your organization. You know, just fill in your name here and it works well. Like, it's not like they came out with those things.
Adam Larson:So you're having to create something out of nothing. And so being able to do that as an organization and doing it well so that in creating that space for people to fail, for people to try things out and test the waters without... As they're learning. And also being able to say, hey, youth who are really good at this, can you show us what you're doing? And let's talk about it.
Adam Larson:Then, hey, we'll pass like it's that back and forth knowledge. I, I, I, it's hugely important for an organization to grow and then for an organization to be willing to say, hey, we don't know how to do this yet. Let's figure this out together and let's let's do this well so that we can grow together. And I I think that goes to transparency within an organization because a lot of times when you're an employee, you don't really see the leaders struggling with what they need to do and then being there open and saying, hey, we're still figuring this out. Okay, cool.
Adam Larson:We're all still figuring this out. It kind of... It it it breeds a better culture, feel like.
Polina Nyer:Yeah. Yeah. It is. Well, the culture, it's extremely valuable in any company.
Adam Larson:And
Polina Nyer:I think also culture plays a role when we have such a rapid change. I can see that in my company, which happens to be in technology, but I think this is really universal. The pace of change is so rapid and the economy also, it's challenging right now. So these are the moments where culture can really make a difference. Having a good culture can keep employees and keep them engaged.
Polina Nyer:I think in moments like this, it's hard to be optimistic. It is different where the economy is pulling. Everything is going well for everyone, for the employer, for the employees. It's in challenging times where culture really shines through and the way that we approach AI because AI is not going anywhere. Now we'll be setting the foundation of how we're going to treat it in decades to come.
Adam Larson:Yeah, definitely. Well, Polina, this has been a wonderful conversation. I really appreciate you coming on and sharing about the personas and just what you've been seeing as you've been working in AI and implementing around different cultures. I really appreciate you sharing your expertise with our audience today.
Polina Nyer:Thank you. It was a pleasure being here.
Announcer:This has been Count Me In, IMA's podcast, providing you with the latest perspectives of thought leaders from the accounting and finance profession. If you like what you heard and you'd like to be counted in for more relevant accounting and finance education, visit IMA's website at www.imanet.org.